Evidence map›Paper›PMID 39466313›Full record

ArticleJournal of medical Internet research2024

Chinese Public Attitudes and Opinions on Health Policies During Public Health Emergencies: Sentiment and Topic Analysis.

Min Liu, Shuo Yuan, Bingyan Li, Yuxi Zhang, Jia Liu, Cuixia Guan, Qingqing Chen, Jiayi Ruan, Lunfang Xie

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Min Liu *Department of Nursing, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.ORCID 0000-0002-9905-6877
Shuo Yuan *Department of Cardiology II, Anhui No.2 Provincial People's Hospital, Hefei, China.ORCID 0009-0002-0725-5244
Bingyan LiThe First Affiliated Hospital of Anhui Medical University, Hefei, China.ORCID 0009-0001-4226-4501
Yuxi ZhangSchool of Nursing, Anhui Medical University, Hefei, China.ORCID 0009-0009-3593-0180
Jia LiuSchool of Nursing, Anhui Medical University, Hefei, China.ORCID 0009-0005-1047-1644
Cuixia GuanSchool of Nursing, Anhui Medical University, Hefei, China.ORCID 0009-0009-5771-5211
Qingqing ChenSchool of Nursing, Anhui Medical University, Hefei, China.ORCID 0009-0000-6860-348X
Jiayi RuanSchool of Nursing, Anhui Medical University, Hefei, China.ORCID 0009-0007-9960-3184
Lunfang XieSchool of Nursing, Anhui Medical University, Hefei, China.ORCID 0009-0003-5551-421X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBy the end of 2021, the new wave of COVID-19 sparked by the Omicron variant spread rapidly due to its highly contagious nature, affecting more than 170 countries worldwide. Nucleic acid testing became the gold standard for diagnosing novel coronavirus infections. As of July 2022, numerous cities and regions in China have implemented regular nucleic acid testing policies, which have had a significant impact on socioeconomics and people's lives. This policy has garnered widespread attention on social media platforms.

objectiveThis study took the newly issued regular nucleic acid testing policy during the COVID-19 pandemic as an example to explore the sentiment responses and fluctuations of netizens toward new policies during public health emergencies. It aimed to propose strategies for managing public opinion on the internet and provide recommendations for policy making and public opinion control.

methodsWe collected blog posts related to nucleic acid testing on Weibo from April 1, 2022, to July 31, 2022. We used the topic modeling technique latent Dirichlet allocation (LDA) to identify the most common topics posted by users. We used Bidirectional Encoder Representations from Transformers (BERT) to calculate the sentiment score of each post. We used an autoregressive integrated moving average (ARIMA) model to examine the relationship between sentiment scores and changes over time. We compared the differences in sentiment scores across various topics, as well as the changes in sentiment before and after the announcement of the nucleic acid price reduction policy (May 22) and the lifting of the lockdown policy in Shanghai (June 1).

resultsWe collected a total of 463,566 Weibo posts, with an average of 3799.72 (SD 1296.06) posts published daily. The LDA topic extraction identified 8 topics, with the most numerous being the Shanghai outbreak, nucleic acid testing price, and transportation. The average sentiment score of the posts was 0.64 (SD 0.31), indicating a predominance of positive sentiment. For all topics, posts with positive sentiment consistently outnumbered those with negative sentiment (χ

conclusionsDuring public health emergencies, the topics of public concern were diverse. Public sentiment toward the regular nucleic acid testing policy was generally positive, but fluctuations occurred following the announcement of key policies. To understand the primary concerns of the public, the government needs to monitor social media posts by citizens. By promptly sharing information on media platforms and engaging in effective communication, the government can bridge the information gap between the public and government agencies, fostering a positive public opinion environment.

Indexed as

COVID-19Health PolicyPublic HealthPublic OpinionChinaEast Asian PeopleEmergenciesHumansPandemicsSocial MediaCOVID-19governance strategiesLDAnucleic acid testingopinion analysispublic health emergenciessentiment analysissocial media

Identifiers

PMID39466313
PMCPMC11555446

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.